Is ventilation heterogeneity related to asthma control?
Bibliographic record
Abstract
In asthma patients, magnetic resonance imaging (MRI) and the lung clearance index (LCI) have revealed persistent ventilation heterogeneity, although its relationship to asthma control is not well understood. Therefore, our goal was to explore the relationship of MRI ventilation defects and the LCI with asthma control and quality of life in patients with severe, poorly controlled asthma.18 patients with severe, poorly controlled asthma (mean±sd 46±12 years, six males/12 females) provided written informed consent to an ethics board approved protocol, and underwent spirometry, LCI and (3)He MRI during a single 2-h visit. Asthma control and quality of life were evaluated using the Asthma Control Questionnaire (ACQ) and Asthma Quality of Life Questionnaire (AQLQ). Ventilation heterogeneity was quantified using the LCI and (3)He MRI ventilation defect percent (VDP).All participants reported poorly controlled disease (mean±sd ACQ score=2.3±0.9) and highly heterogeneous ventilation (mean±sd VDP=12±11% and LCI=10.5±3.0). While VDP and LCI were strongly correlated (r=0.86, p<0.0001), in a multivariate model that included forced expiratory volume in 1 s, VDP and LCI, VDP was the only independent predictor of asthma control (R(2)=0.38, p=0.01). There was also a significantly worse VDP, but not LCI in asthma patients with an ACQ score >2 (p=0.04) and AQLQ score <5 (p=0.04), and a trend towards worse VDP (p=0.053), but not LCI in asthma patients reporting ≥1 exacerbation in the past 6 months.In patients with poorly controlled, severe asthma MRI ventilation, but not LCI was significantly worse in those with worse ACQ and AQLQ.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".